Task: The task is to automatically generate regular expressions from lists of different types of names, e.g. product names. For example for names of countries such a regular expression could be found: “\w+land”.For more information please visit: Further Information
Contact: Roman Kern, rkern@know-center.at

Task: The goal of text segmentation is to split a long text into smaller segments. In this thesis Bayesian inference should be used to achieve such a segmentation.For more information please visit: Further Information
Contact: Roman Kern, rkern@know-center.at

Task: Develop a tool to replicate a data-set without actually replicating the content, but replicating the key characteristics.For more information please visit: Further Information
Contact: Roman Kern, rkern@know-center.at

Task: Develop a plugin for Weka that automatically generated hypothesis from a given data set.For more information please visit: Further Information
Contact: Roman Kern, rkern@know-center.at

Actor Based Simulation Framework (Master Thesis)

Task: Development of a actor based simulation of the movement of people, based on Open Street Map and Wikipedia demographics information.For more information please visit: Further Information
Contact: Roman Kern, rkern@know-center.at

Business Intent (Bachelor Thesis / Master Project)

Task: Implement an algorithm to extact goals of companies from Web pages and to compare different company goals.For more information please visit: Further Information
Contact: Mark Kröll, mkroell@tugraz.at

Open Information Extraction for German (Master Thesis)

Task: Evaluation of the state-of-the-art in the area of Open Information Extraction and a prototypical implementation of such a system for the German language.For more information please visit: Further Information
Contact: Mark Kröll, mkroell@tugraz.at

Language Agnostic Information Extraction (Master Thesis)

Task: Evaluation of cross-lingual and language agnistic methods for Information Extraction and an implementation of such a method for text written in English and German.For more information please visit: Further Information
Contact: Mark Kröll, mkroell@tugraz.at

KNOWLEDGE VISUALISATION

Visualization Toolbox (Master Thesis) New topic available

In this work you will develop a Visualization Toolbox which is an application that allows users to discover, explore, and visually analyze datasets from different sources, e.g. publications from digital libraries such as ACM and IEEE, cultural content from services such as Europeana, or the local file system. The Toolbox relies on semantic data models (vocabularies) and their integration (mapping) to provide foundations for automatically generating and configuring suitable visualizations. In a nutshell, the role of the Toolbox is to support automated process of providing visualizations for exploring and analyzing data sets from different sources.For more information please visit:Further Information
or contact:vsabol@know-center.at

We provide a Recommendation dashboard that includes several interactive visualizations that is connected to the Content-based Recommender System (CB-RS). In this work you will implement an algorithm which tracks and collects users’ interaction with the visualizations and which defines behavioral patterns based on those collections. The patterns will be used to infer users’ next action using CB-RS and in turn to recommend visualizations which address users’ task in the best possible way.For more information please visit:Further Information
or contact:vsabol@know-center.at

In this work you’ll develop time visualisations that present an overview of recommended items and their distribution in time (e.g., happenings that lead to a particular event). The time visualisation should adaptively compress to accommodate restricted screen space, and interactively expand when the user needs to explore data in depth. We are looking for a method that makes the best use of the available screen space at each stage. The data visualised comprises recommendations of cultural, educational, and scientific nature, that should catch the attention of the user while reading, surfing, blogging,etc. You will have to deal with Web-based visualisation technologies, the decision on which technology to use for implementation is yours.For more information or to suggest your own topics please contact:
Eduardo Veas, eveas@know-center.at

In this work you will develop selected information visualization components using WebGL and HTML5 technologies. WebGL is a standardized JavaScript API based on OpenGL ES which, as a subset of the full OpenGL specification, provides access to hardware accelerated rendering of interactive 3D and 2D graphics within a Web browser (without any plug-ins). You will beging by performing an analysis of available WebGL-based visualization frameworks. After that a decision will be taken together with your advisor on whether an existing framework will be used or not. Following that, you will start with main task, which is the implementation of selected visual representations, such as a stream graph, hierarchical timeline, scatterplot matrices, coocurrence matrices, sankey diagramms etc. for large scale data. The goal is to achieve a higher scalability compared to what available SVG or canvas-based implementations support. The resulting visualization components shall handle data sets containing well over 10000 data elements, ranging potentially into millions.For more information or to suggest your own topics please contact:
Vedran Sabol, vsabol@know-center.at

3D Knowledge Visualisation using WebGL (Master Thesis)

In this work you will develop a 3D knowledge visualization component using WebGL, which a JavaScript API based on OpenGL ES providing access to hardware accelerated 3D rendering within a Web browser. Knowledge Visualisation is a discipline dealing with presenting and communicating knowledge through visual interfaces. Your tasks will include the implementation and evaluation of a visualization component for visualising 3D models, such as a building, electronic device or a car, and enriching these with knowledge originating from different sources. For example, encoding temperature information in a building using color, superimposing a sequence of usage instructions on a device, or showing technical information (dimensions, rotation direction, maintenance interval etc.) of various car components. You will develop approaches for retrieving information (such as numeric, textual, semantic or time-dependant data) from the knowledge bases and mapping it onto appropriate visual properties of the 3D model. Finally you will perform an evaluation involving several test users to eliminate usability issues and identify directions for further improvements.For more information or to suggest your own topics please contact:
Eduardo Veas, eveas@know-center.at

Topical Landscape Visualisation (Master Thesis)

In this work you will develop methods for visualizing document collections using the topical landscape visual metaphor. A topical landscape resembles a geographic map where text documents are placed in such a way that similar ones are close to each other, while dissimilar ones are far apart – the so called “similarity layout”. The resulting landscape consist of peaks representing agglomerations of thematically similar documents. Your tasks include one or more of the following (TBD): i) increasing the quality of algorithms for computing the similarity layout, ii) development of a scalable Web-based thematic landscape visualization component iii) implementing and evaluating novel interaction and exploration mechanisms for the landscape. Depending on this choice your development work may target a server component for number crunching and/or a client visualization component. The client component shall be realized using Web technologies (JavaScript, HTML5 Canvas or WebGL).For more information or to suggest your own topics please contact:
Vedran Sabol, vsabol@know-center.at

SOCIAL COMPUTING

Analysis of a Collaborative Health Information System

Task: to crawl an online health community and analyze social network aspects (e.g., identification of groups of interests and experts) or various aspects of information quality (e.g., conflicting statements, trust) with the help of statistical and/or semantic technologies.For more information or to suggest your own topics please contact:
Elisabeth Lex, elex@know-center.at
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Analyzing interaction patterns in Online communities

Task: Analyze interaction patterns in Online communities in respect to sentiment and social support.For more information or to suggest your own topics please contact:
Elisabeth Lex, elex@know-center.at

Information Quality using SNA in Online communities and Social Media

Task: Use methods and tools from the field of Social Network Analysis to investigate information quality aspects in Online communities and Social Media (e.g. analysis of connectivity structures, trust, …)For more information or to suggest your own topics please contact:
Elisabeth Lex, elex@know-center.at

Predicting Event Participation from Social Networks and Media (Master Thesis)

Task: Social Media and Networks bear great potential to predict crowd behavior. This was shown in many contexts in the past such as for instance in detecting earth quakes, predicting elections, etc. The goal of this project is a similar one but focuses on a different kind of problem namely known as predicting event participation from social media and networks. The goal of this project is to analyze large scale social media datasets (over time) obtained for instance from Social Media platforms such as Twitter and to implement a predictive model (based on the observations made) that is able to forcast event participation with high accuracy.For more information or to suggest your own topics please contact:
Christoph Trattner, Know-Center, ctrattner@know-center.at

Where will we be going next? Predicting crowd behavior from Dwolla & Foursquare (Master Thesis)

Task: Location-based social service platforms such as Dwolla have gained tremendously in popularity recently. As shown these data sources bear a great potential to predict not only social interactions between user but also tie strength such as partnership. The goal of this project is to compare different sources of location-based social networks derived for instance from Dwolla Check-ins or Foursquare with each other and to implement a high accurate predictive model that is able to recommend places to users which they most likely want to visit in the near future.For more information or to suggest your own topics please contact:
Christoph Trattner, Know-Center, ctrattner@know-center.at

Task: Social data – such as for instance Facebook likes – bear a great potential to increase the predictive power of Online Recommender engines, as for instance used in Amazon. The goal of this project is to implement a novel social recommender engine for online marketplaces by utilizing not only the users likes but also the users social streams.For more information or to suggest your own topics please contact:
Christoph Trattner, Know-Center, ctrattner@know-center.at

Development of a Learning Analytics Plugin for Moodle (Bachelor Thesis or Master Project)

Based on a Moodle extension that has been developed in a research project (INNOVRET), a further plugin should be developed that analyses learner data and visualises themContact: Alexander Nussbaumer, alexander.nussbaumer@tugraz.at or Simone Kopeinik, simone.kopeinik@tugraz.at

Based on learner data (including social networks), content information, and conceptual models, a Java library should be developed that implements various recommender approaches.Contact: Alexander Nussbaumer, alexander.nussbaumer@tugraz.at or Simone Kopeinik, simone.kopeinik@tugraz.at

NETWORK SCIENCE

Link Navigation in Recommender Systems

Task: Taking the content of a recommender system (using recommendation data for movies, books or music artists) as nodes and the recommendations as links, we can create a network. Given this network, we can then try to answer several questions, such as: Is the network connected? What degree distribution does it have? How does it evolve when we add more links? In this work, we will try to answer these questions as well as looking at the navigability of these systems – i.e., how well can users reach parts of the network by following recommendations?For more information or to suggest your own topics please contact:
Daniel Lamprecht, daniel.lamprecht@tugraz.at
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Topic-biased PageRank

Task: PageRank is an algorithm to rank pages based on the “pages are important if important pages link to them” principle. In this work, wewill explore a topic-biased version of PageRank, where rank is determined not only by links but also by page content. We will explore the factors important for high topic-biased PageRank (such as the relation to the topic or the distribution of links) on a set of Wikipedia articles.For more information or to suggest your own topics please contact:
Daniel Lamprecht, daniel.lamprecht@tugraz.at
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Design, simulation and evaluation of team-meetings

Task: Nearly in every field people are working together in teams. Therefore meetings have become very important in the last few decades. Related to that we ask our self how these meetings should be designed to increase performance and efficiency of teams. Should meetings only be used to apportion work or should team members also transfer newly gained knowledge to all other members? Which new gained knowledge should be shared with other members and what role does the structure of the team (i.e.: ratio of specialists and generalists in the team) play in meetings? From another point of view: Which type of meetings fits perfectly for different structured teams? To answer all this questions several types of meetings need to be designed, simulated and evaluated.For more information or to suggest your own topics please contact:
Florian Geigl, florian.geigl@tugraz.at
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Correlation of Network structure and content

Task: Wikipedia is probably the most common information system in the world. People use it every day to find answers to several questions or to extend their personal knowledge. To find a specific answer in such a system, people need to navigate through the underlying network, where each article represents a node whereas links between articles connect two nodes. Different studies have shown that people are extremely efficient in navigating through this type of networks. Thus we think that there is a correlation between network structure and content of the articles. To investigate whether this correlation exists or not, different network and content analysis have to be performed. A starting point will be the examination of network structure communities in connection to content communities/categories.For more information or to suggest your own topics please contact:
Florian Geigl, florian.geigl@tugraz.at
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